PROJECT SUMMARY Glaucoma is a leading cause of blindness. Early diagnosis and close monitoring of glaucoma are important because the onset is insidious and the damage is irreversible. Advanced imaging modalities such as optical coherence tomography (OCT) have been used in the past 2 decades to improve the objective evaluation of glaucoma. OCT has higher axial spatial resolution than other posterior eye imaging modalities and can precisely measure neural structures. However, structural imaging alone has limited sensitivity for detecting early glaucoma and only moderate correlation with visual field (VF) loss. Using high-speed OCT systems, we have developed novel OCT angiography technologies to image vascular plexuses that supply the retinal nerve fibers and ganglion cells damaged by glaucoma. Our results showed that OCT angiographic parameters have better correlation with VF parameters. We have also found that measurement of focal and sectoral glaucoma damage using high-definition volumetric OCT angiographic and structural parameters improves diagnostic performance. The goal of the proposed project is to further improve the diagnosis and monitoring of glaucoma using ultrahigh-speed OCT and artificial intelligence machine learning techniques. The specific aims are: 1. Develop quantitative wide-field OCT angiography. We will develop a swept-source OCT prototype that is 4 times faster than current commercial OCT systems. The higher speed will be used to fully sample the neural structures and associated capillary plexuses damaged by glaucoma. 2. Simulate VF by combining structural and angiographic OCT. Preliminary results showed that both structural and angiographic OCT parameters have high correlation with VF on a sector basis. It may be possible to accurately simulate VF results by combining these parameters using an artificial neural network. The simulated VF may be more precise and reliable than subjective VF testing. 3. Longitudinal clinical study in glaucoma diagnosis and monitoring. Our novel OCT structural and angiographic parameters have high accuracy in diagnosing glaucoma. Neural network analysis of structural and angiographic data from a larger clinical study could further improve diagnostic accuracy. Longitudinal follow-up will assess if simulated VF could monitor disease progression as well as actual VF. 4. Clinical study to assess the effects of glaucoma treatments. Preliminary results suggest that OCT angiography could detect the improvement in capillary density after glaucoma surgery and the effects of drugs. These intriguing effects will be tested in before-and-after comparison studies. If successful, we will have an OCT diagnostic system that in minutes provides objective information on the location and severity of glaucoma damage. This approach could replace time-consuming and unreliable VF testing. Measuring the improvement in retinal circulation could be a quicker way to detect the benefit of glaucoma therapies that work through neuroprotection or regeneration, compared to monitoring VF.